Collision Avoidance Maneuver Planning Using GA for LEO and GEO Satellite Maintained in Keeping Area

Lee, Sang-Cherl; Kim, Hae-Dong; Suk, Jinyoung

doi:10.5139/ijass.2012.13.4.474

Cited by 13 publications

(7 citation statements)

References 7 publications

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“…Since Φ is linear transformation and (14) do hold, the necessary and sufficient condition of the nonzero solution existence of X 0 is 1 = 2 ≡ (the initial relative position is nonzero). Thus,…”

Section: Theorem 2 If the Initial Relative Distance Of Pursuer And Ementioning

confidence: 99%

“…Bombardelli [13] obtained an optimal maneuver method to numerically maximize the miss distance, which described the arc length separation between the maneuvering rear point and the predicted collision point. Recently, the study of evasive maneuvers has been done with different emphasis, Lee et al [14,15] used genetic algorithm to find a solution of minimum fuel consumption and to determine delta-V maneuvers in LEO and GEO. de Jesus and de Sousa [16] investigated the existence of symmetry in determining the initial conditions of collisions among objects.…”

Section: Introductionmentioning

confidence: 99%

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Analytical Approach for Orbital Evasion with Space Geometry Considered

Wang

Guo

et al. 2017

International Journal of Aerospace Engineering

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This paper researches an optimal problem of orbital evasion with considering space geometry by using an analytical approach. Firstly, an angles-only relative navigation model is built and the definition of completely nonobservable maneuver is proposed. After algebraic analysis of relative space geometry, it is proved that the completely nonobservable maneuver is nonexistent. Based on this, the angle measurements of orbit without evasion are set as reference measurements and an analytical solution is derived to find the minimum difference between measurements and the reference measurements in a constant measuring time. Then, an object function using vector multiplication is designed and an optimization model is established so as to prove the optimality of analytical solution. At last, several numerical simulations are performed with different maneuver directions, which verify the effectiveness of the analytical method of this paper for orbital evasion problem. This method offers a new viewpoint for orbital evasion problem.

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Section: Theorem 2 If the Initial Relative Distance Of Pursuer And Ementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Analytical Approach for Orbital Evasion with Space Geometry Considered

Wang

Guo

et al. 2017

International Journal of Aerospace Engineering

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“…Advantage matrix -Building the advantage matrix which represents the preference of behaviors -Easy to interpret -High cost for building matrix [4], [5] Genetic algorithm -Exploring the solution space to improve particular objectives -Long time to obtain solutions -Local optimum [6], [7], [8], [9] Reinforcement learning -Finding an optimal policy that maximize total future reward -Global optimum -Difficult to define reward for each behavior [10], [11], [12] Matrix factorization -Estimating latent factors that explain the implcit attributes of state features -Latent factor -Robust to the data sparseness [13] timal behavior by using a predefined situation-behavior (SB) matrix [4], [5], [14]. The results obtained by the methods are easy to interpret because optimal behavior is identified by comparing values of elements in the given AM.…”

Section: Introductionmentioning

confidence: 99%

“…Another line of research addresses the optimal behavior inference problem using a genetic algorithm (GA), which improves their objectives by sufficiently exploring the solution space [6], [7], [15], [16]. Although GA did not require building a matrix, chromosome and crossover/mutation schemes should be predefined to perform the algorithm [8], [16].…”

Section: Introductionmentioning

confidence: 99%

A Behavior Optimization Method for Unmanned Combat Aerial Vehicles Using Matrix Factorization

et al. 2020

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One of the fundamental technologies for unmanned combat aerial vehicles and combat simulators is behavior optimization, which finds a behavior that maximizes the probability of winning a battle. With the advent of military science, combat logs became available, allowing machine learning algorithms to be used for the behavior optimization. Due to implicit attributes such as the experience of an operator that are not explicitly presented in log data, existing methods for behavior optimization have limitations in performance improvement. Furthermore, specific behaviors occur with low frequency, resulting in a dataset with imbalanced and empty values. Therefore, we apply a matrix factorization (MF) method, which is one of latent factor models and known for sophisticated imputation of empty values, to the behavior optimization problem of unmanned combat aerial vehicles. A situation-behavior matrix, whose elements are ratings indicating the optimality of behaviors in situations, is defined to implement the MF based method. Experiments for performance comparison were conducted on combat logs, in which the proposed method yielded satisfactory results. INDEX TERMS behavior optimization, unmanned vehicle, matrix factorization, reinforcement learning, situation-behavior matrix ABBREVIATIONS AM Advantage matrix. FOV Field of view. GA Genetic algorithm. LOS Line of sight. MF Matrix factorization. nDCG Normalized discounted cumulative gain. RL Reinforcement learning. SB Situation-behavior. UV Unmanned vehicle.

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“…The genetic algorithm is an iterative scheme where the population is modified using the best features of the 'genes' from previous generations. The selection, crossover and mutation operators are applied to identify the best solution [6,12,13].…”

Section: Global Optimization Algorithmmentioning

confidence: 99%

Comparison of Global Optimization Methods for Insertion Maneuver into Earth-Moon L2 Quasi-Halo Orbit Considering Collision Avoidance

Lee¹,

Kim²,

Yang³

et al. 2014

International Journal of Aeronautical and Space Sciences

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A spacecraft placed in an Earth-Moon L2 quasi-halo orbit can maintain constant communication between the Earth and the far side of the Moon. This quasi-halo orbit could be used to establish a lunar space station and serve as a gateway to explore the solar system. For a mission in an Earth-Moon L2 quasi-halo orbit, a spacecraft would have to be transferred from the Earth to the vicinity of the Earth-Moon L2 point, then inserted into the Earth-Moon L2 quasi-halo orbit. Unlike the near Earth case, this orbit is essentially very unstable due to mutually perturbing gravitational attractions by the Earth, the Moon and the Sun. In this paper, an insertion maneuver of a spacecraft into an Earth-Moon L2 quasi-halo orbit was investigated using the global optimization algorithm, including simulated annealing, genetic algorithm and pattern search method with collision avoidance taken into consideration. The result shows that the spacecraft can maintain its own position in the Earth-Moon L2 quasi-halo orbit and avoid collisions with threatening objects.

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Collision Avoidance Maneuver Planning Using GA for LEO and GEO Satellite Maintained in Keeping Area

Cited by 13 publications

References 7 publications

Analytical Approach for Orbital Evasion with Space Geometry Considered

Analytical Approach for Orbital Evasion with Space Geometry Considered

A Behavior Optimization Method for Unmanned Combat Aerial Vehicles Using Matrix Factorization

Comparison of Global Optimization Methods for Insertion Maneuver into Earth-Moon L2 Quasi-Halo Orbit Considering Collision Avoidance

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